电力物联网中5G边缘计算技术的研究

为解决电力物联网中海量设备接入诉求,云服务器集中处理架构逐渐向边缘计算模式演进, 电力物联网演进为云、网、边、端四层模型。本文根据现有电力业务类型和传输需求,分析了电力物联网中边缘计算面临的技术难点,提出采用基于时隙的灵活调度,并结合自时隙结构和灵活时隙配置等方案改进边缘计算网络中的端到端时延; 采用基于5QI配置及ARP优先级方案提升电力物联网中业务保障效果,采用业务安全隔离是保障电力业务安全运行。文章最后给出了基于5G技术的边缘计算网关体系架设和技术特点,指出可以充分利用基于5G的创新技术, 提升电力物联网中边缘计算有效性和安全性,满足电力物联网蓬勃应用的需要。     

物联网环境下移动边缘计算安全问题

物联网时代多类型流量的接入与应用场景的多样性,从计算能力、存储和业务时延等多个方面对当前集中式云计算架构提出新的挑战.移动边缘计算(MEC)作为一种在网络边缘为用户提供服务的解决方案,能够满足物联网多样性的业务需求.针对移动边缘计算在物联网中的安全问题,对移动边缘计算的概念、应用场景和安全进程进行介绍,着重从数据传输安全、存储安全和计算安全3个方面阐述了移动边缘计算在物联网时代所面临的安全挑战.     

基于泛在电力物联网的输配电设备在线监测应用

随着泛在电力物联网概念的提出,泛在电力物联网建设将广泛应用大数据、云计算、人工智能等信息技术和智能技术.本文研究基于泛在电力物联网的电力设备运行状态在线监测,首先分析了传感器技术在输配电设备在线监测中的应用,然后从数据清理、状态评估、边缘计算三个方面阐述泛在电力物联网中电气设备在线监测的数据处理方式.     

基于物联网的智能电网信息化建设研究

随着科学技术的不断发展,对基于物联网的智能电网信息化建设研究也逐渐成为人们关注的焦点。尤其在信息感知、高效信息处理以及可靠传输方面,物联网发挥重要的作用,也因此成为电力系统信息化技术发展的必然趋势。本文主要对电力物联网的基本概述、电力物联网的功能应用以及电力物联网的关键技术进行探析。     

基于零信任框架的泛在电力物联网安全防护研究

建设泛在电力物联网是充分应用"大云物移智"等现代化信息技术,实现电力系统各个环节的万物互联、人机交互.在这种新场景下,身份认证、数据安全、信息交互等网络安全问题迫切需要解决,本文从零信任框架出发,构建泛在电力物联网安全防护方案,重点从身份认证、动态授权、监测审计三个方面提出防护建议,打破传统以边界防护为基础的网络安全框架,为泛在电力物联网安全防护提供新思路、新方法,以适应泛在电力物联网的业务发展需求.     

电力信息系统安全可信的一站式业务服务系统的研究

本文介绍了电力信息系统安全可信的一站式业务服务系统的总体结构,由信任与授权服务、可信Web Service计算、一站式电力业务服务框架等模块组成;然后详细描述了一站式电力业务服务框架核心功能模块的设计;最后介绍了一站式电力业务服务的身份认证、服务请求、服务调度处理的实现流程。     

泛在电力物联网可信安全接入方案

为全面提高全业务泛在电力物联网安全综合防御能力,解决目前全业务泛在电力物联网安全防护指导和终端认证机制的缺失和不足,本文提出一种泛在电力物联网可信安全接入方案。首先给电力物联网终端层设备确定一个唯一标识的指纹信息;然后结合该指纹信息,采用身份标识密码技术实现终端层设备的接入认证,阻断非法终端的接入;最后设计合法终端的身份信息安全传递机制,根据身份信息对合法终端的异常行为进行溯源。     

基于边缘计算的物联网入侵检测技术综述

物联网架构与设备的特殊性,传统集中式入侵检测方案的局限性以及物联网边缘数据的激增,都对物联网的安全性提出了更高的要求.边缘计算的出现为解决这一问题提供了新的思路.本文首先归纳总结了物联网常见攻击方式,并介绍了边缘计算相关概念;其次,本文对基于边缘计算的物联网入侵检测技术的最新研究进展进行了全面调查;最后讨论了基于边缘计...     

基于边缘计算的电力物联网组网方案研究

根据边缘计算和组网平台技术,设计了一种电力物联网组网系统,能够完成电力物联网的智能自动组网,采用多通道采集技术对电力物联网数据进行信息采集和数据归纳,在边缘计算的基础上融入正交离散多小波变换算法,使系统能够快速判断数据,并进行组网.最后,通过对比验证分析列出三种不同组网方案的数据,从而直观看出本设计的优越性,通过列出的...     

物联网中可信控制技术研究

在物物通信和物联网技术的基础上综述了国内外物联网的发展现状,提出了物联网中无线传感器网络(WSN)与lnternet的互联融合模型.结合此模型,研究了其可信控制关键技术,包括可信路由技术、信任控制技术,以促进物联网在中国的安全发展.     

物联网技术及其安全性研究

针对物联网_技术的发展趋势问题,基于物联网的体系结构和关键技术,分析了物联网的安全需求与相关特性,构建了一个以RFID安全和隐私保护为重点的物联网安全框架,提出了应对物联网所面临的安全挑战的解决途径,最后对物联网未来发展趋势作了展望.     

基于区块链的物联网认证机制综述

随着物联网(Internet of Things, IoT)技术的高速发展,各类智能设备数量激增,身份认证成为保障IoT安全的首要需求.区块链作为一种分布式账本技术,提供了去信任的协作环境和安全的数据管理平台,使用区块链技术驱动IoT认证成为学术界和工业界关注的热点.基于云计算和云边协同两种架构分析IoT身份认证机制设计的主要需求,总结区块链技术应用于IoT场景面临的挑战;梳理现有IoT身份认证机制的工作,并将其归结为基于密钥的认证、基于证书的认证和基于身份的认证;分析应用区块链技术的IoT认证工作,并根据认证对象和附加属性对相关文献进行归纳和总结.从形式化和非形式化两个方向总结基于区块链的IoT认证机制的安全性分析方法.最后展望了未来研究方向.     

A secure fog-based platform for SCADA-based IoT critical infrastructure

The rapid proliferation of Internet of things (IoT) devices, such as smart meters and water valves, into industrial critical infrastructures and control systems has put stringent performance and scalability requirements on modern Supervisory Control and Data Acquisition (SCADA) systems. While cloud computing has enabled modern SCADA systems to cope with the increasing amount of data generated by sensors, actuators, and control devices, there has been a growing interest recently to deploy edge data centers in fog architectures to secure low-latency and enhanced security for mission-critical data. However, fog security and privacy for SCADA-based IoT critical infrastructures remains an under-researched area. To address this challenge, this contribution proposes a novel security “toolbox” to reinforce the integrity, security, and privacy of SCADA-based IoT critical infrastructure at the fog layer. The toolbox incorporates a key feature: a cryptographic-based access approach to the cloud services using identity-based cryptography and signature schemes at the fog layer. We present the implementation details of a prototype for our proposed secure fog-based platform and provide performance evaluation results to demonstrate the appropriateness of the proposed platform in a real-world scenario. These results can pave the way toward the development of a more secure and trusted SCADA-based IoT critical infrastructure, which is essential to counter cyber threats against next-generation critical infrastructure and industrial control systems. The results from the experiments demonstrate a superior performance of the secure fog-based platform, which is around 2.8 seconds when adding five virtual machines (VMs), 3.2 seconds when adding 10 VMs, and 112 seconds when adding 1000 VMs, compared to the multilevel user access control platform.     

IoT edge computing-enabled collaborative tracking system for manufacturing resources in industrial park

In manufacturing industry, the movement of manufacturing resources in production logistics often affects the overall efficiency. This research is motivated by a world-leading air-conditioner manufacturer. In order to provide the right manufacturing resources for subsequent production steps, excessive time and human effort has been consumed in locating the manufacturing resources in a huge industrial park. The development of Internet of Things (IoT) has made a profound impact on establish smart manufacturing workshop and tracking applications, however a growing trend of data quantity that generated from massive, heterogeneous and bottomed manufacturing resources objects pose challenge to centralized decision. In this study, the concept of edge-computing deeply integrated in collaborative tracking purpose in virtue of IoT technology. An IoT edge computing enabled collaborative tracking architecture is developed to offload the computation pressure and realize distributed decision making. A supervised learning of genetic tracking method is innovatively presented to ensure tracking accuracy and effectiveness. Finally, the research output is developed and implemented in a real-life industrial park for verification. The results show that the proposed tracking method not only performs constant improving accuracy up to 96.14% after learning compared to other tracking method, but also ensure quick responsiveness and scalability.     

物联网安全测评技术综述

近年来,物联网大规模应用于智能制造、智能家居、智慧医疗等产业,物联网的安全问题日益突出,给物联网的发展带来了前所未有的挑战。安全测评技术是保障物联网安全的重要手段,在物联网应用的整个开发生命周期都需要进行安全测评工作,以保证物联网服务的安全性和健壮性。物联网节点面临计算能力、体积和功耗受限等挑战,智慧城市等应用场景提出了大规模泛在异构连接和复杂跨域的需求。本文首先总结了目前物联网中常用的安全测评方法和风险管理技术;然后从绿色、智能和开放三个方面分析物联网安全技术的发展现状和存在的安全问题,并总结了物联网安全测评面临的挑战以及未来的研究方向。     

Detecting Vulnerability on IoT Device Firmware: A Survey

Internet of things (IoT) devices make up 30%of all network-connected endpoints,introducing vulnerabilities and novel attacks that make many companies as primary targets for cybercriminals.To address this increasing threat surface,every organization deploying IoT devices needs to consider security risks to ensure those devices are secure and trusted.Among all the solutions for security risks,firmware security analysis is essential to fix software bugs,patch vulnerabilities,or add new security fea...     

基于代理重加密的物联网云节点授权可信更新机制

物联网云平台通过物联网节点采集和使用数据,基于云平台进行数据的运算和存储,提升了物联网处理数据的能力和数据共享的范围,也丰富了云端数据的内容,推动了互联网与人类世界的渗透和融合,同样也带来了全新的安全问题,由于物联网节点的特点与局限性,导致节点极其容易受到攻击,因此,如何实现物联网云平台中被劫持节点数据访问授权的可信更新至关重要.为此,提出了一种基于代理重加密的物联网云节点授权可信更新机制(PRE based trusted update scheme of authorization for nodes on IoT cloud platform, PRE-TUAN).首先,定义系统模型,包含可信的物联网数据服务器、授权管理服务器和半可信的云端重加密代理服务器;其次,描述系统流程和算法;最后对PRE-TUAN进行安全性分析和证明.PRE-TUAN以代理重加密为基础,将充分发挥云的运算能力,同时确保物联网数据分享的安全与可靠.     

Deep learning models for cloud,edge, fog,and IoT computing paradigms: Survey,recent advances,and future directions

In recent times, the machine learning (ML) community has recognized the deep learning (DL) computing model as the Gold Standard. DL has gradually become the most widely used computational approach in the field of machine learning, achieving remarkable results in various complex cognitive tasks that are comparable to, or even surpassing human performance. One of the key benefits of DL is its ability to learn from vast amounts of data. In recent years, the DL field has witnessed rapid expansion and has found successful applications in various conventional areas. Significantly, DL has outperformed established ML techniques in multiple domains, such as cloud computing, robotics, cybersecurity, and several others. Nowadays, cloud computing has become crucial owing to the constant growth of the IoT network. It remains the finest approach for putting sophisticated computational applications into use, stressing the huge data processing. Nevertheless, the cloud falls short because of the crucial limitations of cutting-edge IoT applications that produce enormous amounts of data and necessitate a quick reaction time with increased privacy. The latest trend is to adopt a decentralized distributed architecture and transfer processing and storage resources to the network edge. This eliminates the bottleneck of cloud computing as it places data processing and analytics closer to the consumer. Machine learning (ML) is being increasingly utilized at the network edge to strengthen computer programs, specifically by reducing latency and energy consumption while enhancing resource management and security. To achieve optimal outcomes in terms of efficiency, space, reliability, and safety with minimal power usage, intensive research is needed to develop and apply machine learning algorithms. This comprehensive examination of prevalent computing paradigms underscores recent advancements resulting from the integration of machine learning and emerging computing models, while also addressing the underlying open research issues along with potential future directions. Because it is thought to open up new opportunities for both interdisciplinary research and commercial applications, we present a thorough assessment of the most recent works involving the convergence of deep learning with various computing paradigms, including cloud, fog, edge, and IoT, in this contribution. We also draw attention to the main issues and possible future lines of research. We hope this survey will spur additional study and contributions in this exciting area.     

基于可信执行环境的物联网边缘流处理安全技术综述

万物互联时代,物联网中感知设备持续产生大量的敏感数据。实时且安全的数据流处理是面向物联网关键应用中需要解决的一个挑战。在近年兴起的边缘计算模式下,借助靠近终端的设备执行计算密集型任务与存储大量的终端设备数据,物联网中数据流处理的安全性和实时性可以得到有效的提升。然而,在基于边缘的物联网流处理架构下,数据被暴露在边缘设备易受攻击的软件堆栈中,从而给边缘带来了新的安全威胁。为此,文章对基于可信执行环境的物联网边缘流处理安全技术进行研究。从边缘出发,介绍边缘安全流处理相关背景并探讨边缘安全流处理的具体解决方案,接着分析主流方案的实验结果,最后展望未来研究方向。